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    <article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/using-boosting-to-learn-from-errors.html" class="u-url">using-boosting-to-learn-from-errors</a></h1>
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            <p class="byline author vcard"><span class="byline-name fn">
                Tao Junjie
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            <p class="dateline"><a href="posts/using-boosting-to-learn-from-errors.html" rel="bookmark"><time class="published dt-published" datetime="2015-08-18T12:57:47+08:00" title="2015-08-18 12:57">2015-08-18 12:57</time></a></p>
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<h2 id="用梯度提升回归从误差中学习">用梯度提升回归从误差中学习<a class="anchor-link" href="posts/using-boosting-to-learn-from-errors.html#%E7%94%A8%E6%A2%AF%E5%BA%A6%E6%8F%90%E5%8D%87%E5%9B%9E%E5%BD%92%E4%BB%8E%E8%AF%AF%E5%B7%AE%E4%B8%AD%E5%AD%A6%E4%B9%A0">¶</a>
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<p>梯度提升回归（Gradient boosting regression，GBR）是一种从它的错误中进行学习的技术。它本质上就是集思广益，集成一堆较差的学习算法进行学习。有两点需要注意：</p>
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<li>每个学习算法准备率都不高，但是它们集成起来可以获得很好的准确率。</li>
<li>这些学习算法依次应用，也就是说每个学习算法都是在前一个学习算法的错误中学习</li>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/using-linear-methods-for-classification-logistic-regression.html" class="u-url">using-linear-methods-for-classification-logistic-regression</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/using-linear-methods-for-classification-logistic-regression.html" rel="bookmark"><time class="published dt-published" datetime="2015-08-18T12:57:47+08:00" title="2015-08-18 12:57">2015-08-18 12:57</time></a></p>
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<h2 id="用线性方法处理分类问题——逻辑回归">用线性方法处理分类问题——逻辑回归<a class="anchor-link" href="posts/using-linear-methods-for-classification-logistic-regression.html#%E7%94%A8%E7%BA%BF%E6%80%A7%E6%96%B9%E6%B3%95%E5%A4%84%E7%90%86%E5%88%86%E7%B1%BB%E9%97%AE%E9%A2%98%E2%80%94%E2%80%94%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92">¶</a>
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<p>实际上线性模型也可以用于分类任务。方法是把一个线性模型拟合成某个类型的概率分布，然后用一个函数建立阈值来确定结果属于哪一类。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/using-ridge-regression-to-overcome-linear-regression-shortfalls.html" class="u-url">using-ridge-regression-to-overcome-linear-regression-shortfalls</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/using-ridge-regression-to-overcome-linear-regression-shortfalls.html" rel="bookmark"><time class="published dt-published" datetime="2015-08-18T12:57:47+08:00" title="2015-08-18 12:57">2015-08-18 12:57</time></a></p>
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<h2 id="用岭回归弥补线性回归的不足">用岭回归弥补线性回归的不足<a class="anchor-link" href="posts/using-ridge-regression-to-overcome-linear-regression-shortfalls.html#%E7%94%A8%E5%B2%AD%E5%9B%9E%E5%BD%92%E5%BC%A5%E8%A1%A5%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E7%9A%84%E4%B8%8D%E8%B6%B3">¶</a>
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<p>本主题将介绍岭回归。和线性回归不同，它引入了正则化参数来“缩减”相关系数。当数据集中存在共线因素时，岭回归会很有用。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/using-sparsity-to-regularize-models.html" class="u-url">using-sparsity-to-regularize-models</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/using-sparsity-to-regularize-models.html" rel="bookmark"><time class="published dt-published" datetime="2015-08-18T12:57:47+08:00" title="2015-08-18 12:57">2015-08-18 12:57</time></a></p>
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<h2 id="LASSO正则化">LASSO正则化<a class="anchor-link" href="posts/using-sparsity-to-regularize-models.html#LASSO%E6%AD%A3%E5%88%99%E5%8C%96">¶</a>
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<p>LASSO（ least absolute shrinkage and selection operator，最小绝对值收缩和选择算子）方法与岭回归和LARS（least angle regression，最小角回归）很类似。与岭回归类似，它也是通过增加惩罚函数来判断、消除特征间的共线性。与LARS相似的是它也可以用作参数选择，通常得出一个相关系数的稀疏向量。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/dlott-15095-2015-08-17-report.html" class="u-url">大乐透15095期(2015-08-17)数据分析报告</a></h1>
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